{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# stacking_fault_map_2D calculation style\n",
    "\n",
    "**Lucas M. Hale**, [lucas.hale@nist.gov](mailto:lucas.hale@nist.gov?Subject=ipr-demo), *Materials Science and Engineering Division, NIST*.\n",
    "\n",
    "## Introduction\n",
    "\n",
    "The __stacking_fault_map_2D__ calculation style evaluates the full 2D generalized stacking fault map for an array of shifts along a specified crystallographic plane.  A regular grid of points is established and the generalized stacking fault energy is evaluated at each.\n",
    "\n",
    "### Version notes\n",
    "\n",
    "- 2018-07-09: Notebook added.\n",
    "- 2019-07-30: Description updated and small changes due to iprPy version.\n",
    "- 2020-05-22: Version 0.10 update - potentials now loaded from database.\n",
    "- 2020-09-22: Calculation updated to use atomman.defect.StackingFault class. Setup and parameter definition streamlined.\n",
    "\n",
    "### Additional dependencies\n",
    "\n",
    "### Disclaimers\n",
    "\n",
    "- [NIST disclaimers](http://www.nist.gov/public_affairs/disclaimer.cfm)\n",
    "- The system's dimension perpendicular to the fault plane should be large to minimize the interaction of the free surface and the stacking fault.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method and Theory\n",
    "\n",
    "First, an initial system is generated.  This is accomplished using atomman.defect.StackingFault, which\n",
    "\n",
    "1. Starts with a unit cell system.\n",
    "\n",
    "2. Generates a transformed system by rotating the unit cell such that the new\n",
    "   system's box vectors correspond to crystallographic directions, and filled\n",
    "   in with atoms to remain a perfect bulk cell when the three boundaries are\n",
    "   periodic.\n",
    "\n",
    "3. All atoms are shifted by a fractional amount of the box vectors if needed.\n",
    "\n",
    "4. A supercell system is constructed by combining multiple replicas of the\n",
    "   transformed system.\n",
    "\n",
    "5. The system is then cut by making one of the box boundaries non-periodic.  A limitation placed on the calculation is that the normal to the cut plane must correspond to one of the three Cartesian ($x$, $y$, or $z$) axes.  If true, then of the system's three box vectors ($\\vec{a}$, $\\vec{b}$, and $\\vec{c}$), two will be parallel to the plane, and the third will not.  The non-parallel box vector is called the cutboxvector, and for LAMMPS compatible systems, the following conditions can be used to check the system's compatibility:\n",
    "\n",
    "   - cutboxvector = 'c': all systems allowed.\n",
    "\n",
    "   - cutboxvector = 'b': the system's yz tilt must be zero.\n",
    "\n",
    "   - cutboxvector = 'a': the system's xy and xz tilts must be zero.\n",
    "\n",
    "A LAMMPS simulation performs an energy/force minimization on the system where the atoms are confined to only relax along the Cartesian direction normal to the cut plane.\n",
    "\n",
    "A mathematical fault plane parallel to the cut plane is defined in the middle of the system.  A generalized stacking fault system can then be created by shifting all atoms on one side of the fault plane by a vector, $\\vec{s}$.  The shifted system is then relaxed using the same confined energy/force minimization used on the non-shifted system.  The generalized stacking fault energy, $\\gamma$, can then be computed by comparing the total energy of the system, $E_{total}$, before and after $\\vec{s}$ is applied\n",
    "\n",
    "$$ \\gamma(\\vec{s}) = \\frac{E_{total}(\\vec{s}) - E_{total}(\\vec{0})}{A},$$\n",
    "\n",
    "where $A$ is the area of the fault plane, which can be computed using the two box vectors, $\\vec{a_1}$ and $\\vec{a_2}$, that are not the cutboxvector.\n",
    "\n",
    "$$A = \\left| \\vec{a_1} \\times \\vec{a_2} \\right|,$$\n",
    "\n",
    "Additionally, the relaxation normal to the glide plane is characterized using the center of mass of the atoms above and below the cut plane.  Notably, the component of the center of mass normal to the glide/cut plane is calculated for the two halves of the the system, and the difference is computed\n",
    "\n",
    "$$ \\delta = \\left<x\\right>^{+} - \\left<x\\right>^{-}.$$\n",
    "\n",
    "The relaxation normal is then taken as the change in the center of mass difference after the shift is applied.\n",
    "\n",
    "$$ \\Delta\\delta = \\delta(\\vec{s}) - \\delta(\\vec{0}).$$\n",
    "\n",
    "The stacking_fault_map_2D calculation evaluates both $\\gamma$ and $\\Delta\\delta$ for a complete 2D grid of $\\vec{s}$ values.  The grid is built by taking fractional steps along two vectors parallel to the shift plane.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demonstration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Library imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries needed by the calculation. The external libraries used are:\n",
    "\n",
    "- [numpy](http://www.numpy.org/)\n",
    "\n",
    "- [matplotlib](https://matplotlib.org/)\n",
    "\n",
    "- [atomman](https://github.com/usnistgov/atomman)\n",
    "\n",
    "- [iprPy](https://github.com/usnistgov/iprPy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook last executed on 2020-09-22 using iprPy version 0.10.2\n"
     ]
    }
   ],
   "source": [
    "# Standard library imports\n",
    "from pathlib import Path\n",
    "import os\n",
    "import datetime\n",
    "\n",
    "# http://www.numpy.org/\n",
    "import numpy as np \n",
    "\n",
    "# https://matplotlib.org/\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# https://github.com/usnistgov/atomman \n",
    "import atomman as am\n",
    "import atomman.lammps as lmp\n",
    "import atomman.unitconvert as uc\n",
    "\n",
    "# https://github.com/usnistgov/iprPy\n",
    "import iprPy\n",
    "\n",
    "print('Notebook last executed on', datetime.date.today(), 'using iprPy version', iprPy.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2. Default calculation setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify calculation style\n",
    "calc_style = 'stacking_fault_map_2D'\n",
    "\n",
    "# If workingdir is already set, then do nothing (already in correct folder)\n",
    "try:\n",
    "    workingdir = workingdir\n",
    "\n",
    "# Change to workingdir if not already there\n",
    "except:\n",
    "    workingdir = Path('calculationfiles', calc_style)\n",
    "    if not workingdir.is_dir():\n",
    "        workingdir.mkdir(parents=True)\n",
    "    os.chdir(workingdir)\n",
    "            \n",
    "# Initialize connection to library\n",
    "with open('C:/Users/lmh1/Documents/potentials_nist_gov/password.txt') as f:\n",
    "    user, pswd = f.read().strip().split()\n",
    "library = iprPy.Library(load=['lammps_potentials'], username=user, password=pswd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Assign values for the calculation's run parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Specify system-specific paths\n",
    "\n",
    "- __lammps_command__ is the LAMMPS command to use.\n",
    "\n",
    "- __mpi_command__ MPI command for running LAMMPS in parallel. A value of None will run simulations serially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lammps_command = 'lmp_serial'\n",
    "mpi_command = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2. Load interatomic potential\n",
    "\n",
    "- __potential_name__ gives the name of the potential_LAMMPS reference record in the iprPy library to use for the calculation.  \n",
    "\n",
    "- __potential__ is an atomman.lammps.Potential object (required)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "potential_name = '1999--Mishin-Y--Ni--LAMMPS--ipr1'\n",
    "\n",
    "# Retrieve potential and parameter file(s)\n",
    "potential = library.get_lammps_potential(id=potential_name, getfiles=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3. Load initial unit cell system\n",
    "\n",
    "- __ucell__ is an atomman.System representing a fundamental unit cell of the system (required).  Here, this is loaded from the database for the prototype."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avect =  [ 3.520,  0.000,  0.000]\n",
      "bvect =  [ 0.000,  3.520,  0.000]\n",
      "cvect =  [ 0.000,  0.000,  3.520]\n",
      "origin = [ 0.000,  0.000,  0.000]\n",
      "natoms = 4\n",
      "natypes = 1\n",
      "symbols = ('Ni',)\n",
      "pbc = [ True  True  True]\n",
      "per-atom properties = ['atype', 'pos']\n",
      "     id |   atype |  pos[0] |  pos[1] |  pos[2]\n",
      "      0 |       1 |   0.000 |   0.000 |   0.000\n",
      "      1 |       1 |   0.000 |   1.760 |   1.760\n",
      "      2 |       1 |   1.760 |   0.000 |   1.760\n",
      "      3 |       1 |   1.760 |   1.760 |   0.000\n"
     ]
    }
   ],
   "source": [
    "# Create ucell by loading prototype record\n",
    "ucell = am.load('crystal', potential=potential, family='A1--Cu--fcc', database=library)\n",
    "\n",
    "print(ucell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4. Specify the defect parameters\n",
    "\n",
    "- __hkl__ gives the Miller (hkl) or Miller-Bravais (hkil) plane to create the free surface on.\n",
    "\n",
    "- __cutboxvector__ specifies which of the three box vectors ('a', 'b', or 'c') is to be made non-periodic to create the free surface. \n",
    "\n",
    "- __shiftindex__ can be used for complex crystals to specify different termination planes.\n",
    "\n",
    "- __a1vect_uvw, a2vect_uvw__ specify two non-parallel Miller crystal vectors within the fault plane corresponding to full planar shifts from one perfect crystal configuration to another.  \n",
    "\n",
    "- __num_a1, num_a2__ specify how many measurements to make along a1vect_uvw, a2vect_uvw respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hkl = [1, 1, 1]\n",
    "cutboxvector = 'c'\n",
    "shiftindex = 0\n",
    "a1vect_uvw = [ 0.0,-0.5, 0.5]\n",
    "a2vect_uvw = [ 0.5,-0.5, 0.0]\n",
    "num_a1 = 20\n",
    "num_a2 = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.5. Modify system\n",
    "\n",
    "- __sizemults__  list of three integers specifying how many times the ucell vectors of $a$, $b$ and $c$ are replicated in creating system.\n",
    "- __minwidth__ specifies a minimum width that the system should be along the cutboxvector direction. The given sizemult in that direction will be increased if needed to ensure that the system is at least this wide.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sizemults = [5, 5, 10]\n",
    "minwidth = uc.set_in_units(0.0, 'angstrom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.6. Specify calculation-specific run parameters\n",
    "\n",
    "- __energytolerance__ is the energy tolerance to use during the minimizations. This is unitless.\n",
    "\n",
    "- __forcetolerance__ is the force tolerance to use during the minimizations. This is in energy/length units.\n",
    "\n",
    "- __maxiterations__ is the maximum number of minimization iterations to use.\n",
    "\n",
    "- __maxevaluations__ is the maximum number of minimization evaluations to use.\n",
    "\n",
    "- __maxatommotion__ is the largest distance that an atom is allowed to move during a minimization iteration. This is in length units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "energytolerance = 1e-8\n",
    "forcetolerance = uc.set_in_units(0.0, 'eV/angstrom')\n",
    "maxiterations = 10000\n",
    "maxevaluations = 100000\n",
    "maxatommotion = uc.set_in_units(0.01, 'angstrom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Define calculation function(s) and generate template LAMMPS script(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1. sfmin.template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('sfmin.template', 'w') as f:\n",
    "    f.write(\"\"\"#LAMMPS input script that performs an energy minimization\n",
    "#for a system with a stacking fault\n",
    "\n",
    "box tilt large\n",
    "\n",
    "<atomman_system_pair_info>\n",
    "\n",
    "<fix_cut_setforce>\n",
    "\n",
    "thermo_style custom step lx ly lz pxx pyy pzz pe\n",
    "thermo_modify format float %.13e\n",
    "\n",
    "compute peatom all pe/atom \n",
    "\n",
    "min_modify dmax <dmax>\n",
    "\n",
    "dump dumpit all custom <maxeval> <sim_directory>*.dump id type x y z c_peatom\n",
    "dump_modify dumpit format <dump_modify_format>\n",
    "\n",
    "minimize <etol> <ftol> <maxiter> <maxeval>\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2. stackingfaultrelax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stackingfaultrelax(lammps_command, system, potential,\n",
    "                       mpi_command=None, sim_directory=None,\n",
    "                       cutboxvector='c',\n",
    "                       etol=0.0, ftol=0.0,\n",
    "                       maxiter=10000, maxeval=100000,\n",
    "                       dmax=uc.set_in_units(0.01, 'angstrom'),\n",
    "                       lammps_date=None):\n",
    "    \"\"\"\n",
    "    Perform a stacking fault relaxation simulation for a single faultshift.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    lammps_command :str\n",
    "        Command for running LAMMPS.\n",
    "    system : atomman.System\n",
    "        The system containing a stacking fault.\n",
    "    potential : atomman.lammps.Potential\n",
    "        The LAMMPS implemented potential to use.\n",
    "    mpi_command : str, optional\n",
    "        The MPI command for running LAMMPS in parallel.  If not given, LAMMPS\n",
    "        will run serially.\n",
    "    sim_directory : str, optional\n",
    "        The path to the directory to perform the simulation in.  If not\n",
    "        given, will use the current working directory.\n",
    "    cutboxvector : str, optional\n",
    "        Indicates which of the three system box vectors, 'a', 'b', or 'c', has\n",
    "        the non-periodic boundary (default is 'c').  Fault plane normal is\n",
    "        defined by the cross of the other two box vectors.\n",
    "    etol : float, optional\n",
    "        The energy tolerance for the structure minimization. This value is\n",
    "        unitless. (Default is 0.0).\n",
    "    ftol : float, optional\n",
    "        The force tolerance for the structure minimization. This value is in\n",
    "        units of force. (Default is 0.0).\n",
    "    maxiter : int, optional\n",
    "        The maximum number of minimization iterations to use (default is \n",
    "        10000).\n",
    "    maxeval : int, optional\n",
    "        The maximum number of minimization evaluations to use (default is \n",
    "        100000).\n",
    "    dmax : float, optional\n",
    "        The maximum distance in length units that any atom is allowed to relax\n",
    "        in any direction during a single minimization iteration (default is\n",
    "        0.01 Angstroms).\n",
    "    lammps_date : datetime.date or None, optional\n",
    "        The date version of the LAMMPS executable.  If None, will be identified\n",
    "        from the lammps_command (default is None).\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    dict\n",
    "        Dictionary of results consisting of keys:\n",
    "        \n",
    "        - **'logfile'** (*str*) - The filename of the LAMMPS log file.\n",
    "        - **'dumpfile'** (*str*) - The filename of the LAMMPS dump file\n",
    "          of the relaxed system.\n",
    "        - **'system'** (*atomman.System*) - The relaxed system.\n",
    "        - **'E_total'** (*float*) - The total potential energy of the relaxed\n",
    "          system.\n",
    "    \n",
    "    Raises\n",
    "    ------\n",
    "    ValueError\n",
    "        For invalid cutboxvectors.\n",
    "    \"\"\"\n",
    "    # Build filedict if function was called from iprPy\n",
    "    try:\n",
    "        assert __name__ == pkg_name\n",
    "        calc = iprPy.load_calculation(calculation_style)\n",
    "        filedict = calc.filedict\n",
    "    except:\n",
    "        filedict = {}\n",
    "    \n",
    "    # Give correct LAMMPS fix setforce command\n",
    "    if cutboxvector == 'a':\n",
    "        fix_cut_setforce = 'fix cut all setforce NULL 0 0'    \n",
    "    elif cutboxvector == 'b':\n",
    "        fix_cut_setforce = 'fix cut all setforce 0 NULL 0'\n",
    "    elif cutboxvector == 'c':\n",
    "        fix_cut_setforce = 'fix cut all setforce 0 0 NULL'    \n",
    "    else: \n",
    "        raise ValueError('Invalid cutboxvector')\n",
    "    \n",
    "    if sim_directory is not None:\n",
    "        # Create sim_directory if it doesn't exist\n",
    "        sim_directory = Path(sim_directory)\n",
    "        if not sim_directory.is_dir():\n",
    "            sim_directory.mkdir()\n",
    "        sim_directory = sim_directory.as_posix()+'/'\n",
    "    else:\n",
    "        # Set sim_directory if is None\n",
    "        sim_directory = ''\n",
    "    \n",
    "    # Get lammps units\n",
    "    lammps_units = lmp.style.unit(potential.units)\n",
    "    \n",
    "    #Get lammps version date\n",
    "    if lammps_date is None:\n",
    "        lammps_date = lmp.checkversion(lammps_command)['date']\n",
    "    \n",
    "    # Define lammps variables\n",
    "    lammps_variables = {}\n",
    "    system_info = system.dump('atom_data',\n",
    "                              f=Path(sim_directory, 'system.dat').as_posix(),\n",
    "                              potential=potential,\n",
    "                              return_pair_info=True)\n",
    "    lammps_variables['atomman_system_pair_info'] = system_info\n",
    "    lammps_variables['fix_cut_setforce'] = fix_cut_setforce\n",
    "    lammps_variables['sim_directory'] = sim_directory\n",
    "    lammps_variables['etol'] = etol\n",
    "    lammps_variables['ftol'] = uc.get_in_units(ftol, lammps_units['force'])\n",
    "    lammps_variables['maxiter'] = maxiter\n",
    "    lammps_variables['maxeval'] = maxeval\n",
    "    lammps_variables['dmax'] = uc.get_in_units(dmax, lammps_units['length'])\n",
    "    \n",
    "    # Set dump_modify format based on dump_modify_version\n",
    "    if lammps_date < datetime.date(2016, 8, 3):\n",
    "        lammps_variables['dump_modify_format'] = '\"%i %i %.13e %.13e %.13e %.13e\"'\n",
    "    else:\n",
    "        lammps_variables['dump_modify_format'] = 'float %.13e'\n",
    "    \n",
    "    # Write lammps input script\n",
    "    template_file = 'sfmin.template'\n",
    "    lammps_script = Path(sim_directory, 'sfmin.in')\n",
    "    template = iprPy.tools.read_calc_file(template_file, filedict)\n",
    "    with open(lammps_script, 'w') as f:\n",
    "        f.write(iprPy.tools.filltemplate(template, lammps_variables,\n",
    "                                         '<', '>'))\n",
    "    \n",
    "    # Run LAMMPS\n",
    "    output = lmp.run(lammps_command, lammps_script.as_posix(), mpi_command,\n",
    "                     logfile=Path(sim_directory, 'log.lammps').as_posix())\n",
    "    \n",
    "    # Extract output values\n",
    "    thermo = output.simulations[-1]['thermo']\n",
    "    logfile = Path(sim_directory, 'log.lammps').as_posix()\n",
    "    dumpfile = Path(sim_directory, '%i.dump' % thermo.Step.values[-1]).as_posix()\n",
    "    E_total = uc.set_in_units(thermo.PotEng.values[-1],\n",
    "                              lammps_units['energy'])\n",
    "    \n",
    "    # Load relaxed system\n",
    "    system = am.load('atom_dump', dumpfile, symbols=system.symbols)\n",
    "    \n",
    "    # Return results\n",
    "    results_dict = {}\n",
    "    results_dict['logfile'] = logfile\n",
    "    results_dict['dumpfile'] = dumpfile\n",
    "    results_dict['system'] = system\n",
    "    results_dict['E_total'] = E_total\n",
    "    \n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4. stackingfaultmap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stackingfaultmap(lammps_command, ucell, potential, hkl,\n",
    "                     mpi_command=None, sizemults=None, minwidth=None, even=False,\n",
    "                     a1vect_uvw=None, a2vect_uvw=None, conventional_setting='p',\n",
    "                     cutboxvector='c', faultpos_rel=None, faultpos_cart=None,\n",
    "                     num_a1=10, num_a2=10, atomshift=None, shiftindex=None,\n",
    "                     etol=0.0, ftol=0.0, maxiter=10000, maxeval=100000,\n",
    "                     dmax=uc.set_in_units(0.01, 'angstrom')): \n",
    "    \"\"\"\n",
    "    Computes a generalized stacking fault map for shifts along a regular 2D\n",
    "    grid.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    lammps_command :str\n",
    "        Command for running LAMMPS.\n",
    "    ucell : atomman.System\n",
    "        The crystal unit cell to use as the basis of the stacking fault\n",
    "        configurations.\n",
    "    potential : atomman.lammps.Potential\n",
    "        The LAMMPS implemented potential to use.\n",
    "    hkl : array-like object\n",
    "        The Miller(-Bravais) crystal fault plane relative to ucell.\n",
    "    mpi_command : str, optional\n",
    "        The MPI command for running LAMMPS in parallel.  If not given, LAMMPS\n",
    "        will run serially.\n",
    "    sizemults : list or tuple, optional\n",
    "        The three System.supersize multipliers [a_mult, b_mult, c_mult] to use on the\n",
    "        rotated cell to build the final system. Note that the cutboxvector sizemult\n",
    "        must be an integer and not a tuple.  Default value is [1, 1, 1].\n",
    "    minwidth : float, optional\n",
    "        If given, the sizemult along the cutboxvector will be selected such that the\n",
    "        width of the resulting final system in that direction will be at least this\n",
    "        value. If both sizemults and minwidth are given, then the larger of the two\n",
    "        in the cutboxvector direction will be used. \n",
    "    even : bool, optional\n",
    "        A True value means that the sizemult for cutboxvector will be made an even\n",
    "        number by adding 1 if it is odd.  Default value is False.\n",
    "    a1vect_uvw : array-like object, optional\n",
    "        The crystal vector to use for one of the two shifting vectors.  If\n",
    "        not given, will be set to the shortest in-plane lattice vector.\n",
    "    a2vect_uvw : array-like object, optional\n",
    "        The crystal vector to use for one of the two shifting vectors.  If\n",
    "        not given, will be set to the shortest in-plane lattice vector not\n",
    "        parallel to a1vect_uvw.\n",
    "    conventional_setting : str, optional\n",
    "        Allows for rotations of a primitive unit cell to be determined from\n",
    "        (hkl) indices specified relative to a conventional unit cell.  Allowed\n",
    "        settings: 'p' for primitive (no conversion), 'f' for face-centered,\n",
    "        'i' for body-centered, and 'a', 'b', or 'c' for side-centered.  Default\n",
    "        behavior is to perform no conversion, i.e. take (hkl) relative to the\n",
    "        given ucell.\n",
    "    cutboxvector : str, optional\n",
    "        Indicates which of the three system box vectors, 'a', 'b', or 'c', to\n",
    "        cut with a non-periodic boundary (default is 'c').\n",
    "    faultpos_rel : float, optional\n",
    "        The position to place the slip plane within the system given as a\n",
    "        relative coordinate along the out-of-plane direction.  faultpos_rel\n",
    "        and faultpos_cart cannot both be given.  Default value is 0.5 if \n",
    "        faultpos_cart is also not given.\n",
    "    faultpos_cart : float, optional\n",
    "        The position to place the slip plane within the system given as a\n",
    "        Cartesian coordinate along the out-of-plane direction.  faultpos_rel\n",
    "        and faultpos_cart cannot both be given.\n",
    "    num_a1 : int, optional\n",
    "        The number of fractional coordinates to evaluate along a1vect_uvw.\n",
    "        Default value is 10.\n",
    "    num_a2 : int, optional\n",
    "        The number of fractional coordinates to evaluate along a2vect_uvw.\n",
    "        Default value is 10.\n",
    "    atomshift : array-like object, optional\n",
    "        A Cartesian vector shift to apply to all atoms.  Can be used to shift\n",
    "        atoms perpendicular to the fault plane to allow different termination\n",
    "        planes to be cut.  Cannot be given with shiftindex.\n",
    "    shiftindex : int, optional\n",
    "        Allows for selection of different termination planes based on the\n",
    "        preferred shift values determined by the underlying fault generation.\n",
    "        Cannot be given with atomshift. If neither atomshift nor shiftindex\n",
    "        given, then shiftindex will be set to 0.\n",
    "    etol : float, optional\n",
    "        The energy tolerance for the structure minimization. This value is\n",
    "        unitless. (Default is 0.0).\n",
    "    ftol : float, optional\n",
    "        The force tolerance for the structure minimization. This value is in\n",
    "        units of force. (Default is 0.0).\n",
    "    maxiter : int, optional\n",
    "        The maximum number of minimization iterations to use (default is \n",
    "        10000).\n",
    "    maxeval : int, optional\n",
    "        The maximum number of minimization evaluations to use (default is \n",
    "        100000).\n",
    "    dmax : float, optional\n",
    "        The maximum distance in length units that any atom is allowed to relax\n",
    "        in any direction during a single minimization iteration (default is\n",
    "        0.01 Angstroms).\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    dict\n",
    "        Dictionary of results consisting of keys:\n",
    "        \n",
    "        - **'A_fault'** (*float*) - The area of the fault surface.\n",
    "        - **'gamma'** (*atomman.defect.GammaSurface*) - A gamma surface\n",
    "          plotting object.\n",
    "    \"\"\"\n",
    "    # Construct stacking fault configuration generator\n",
    "    gsf_gen = am.defect.StackingFault(hkl, ucell, cutboxvector=cutboxvector,\n",
    "                                      a1vect_uvw=a1vect_uvw, a2vect_uvw=a2vect_uvw,\n",
    "                                      conventional_setting=conventional_setting)\n",
    "    \n",
    "    # Check shift parameters\n",
    "    if shiftindex is not None:\n",
    "        assert atomshift is None, 'shiftindex and atomshift cannot both be given'\n",
    "        atomshift = gsf_gen.shifts[shiftindex]\n",
    "    elif atomshift is None:\n",
    "        atomshift = gsf_gen.shifts[0]\n",
    "    \n",
    "    # Generate the free surface (zero-shift) configuration\n",
    "    gsf_gen.surface(shift=atomshift, minwidth=minwidth, sizemults=sizemults,\n",
    "                    even=even, faultpos_rel=faultpos_rel)\n",
    "    \n",
    "    abovefault = gsf_gen.abovefault\n",
    "    cutindex = gsf_gen.cutindex\n",
    "    A_fault = gsf_gen.surfacearea\n",
    "\n",
    "    # Identify lammps_date version\n",
    "    lammps_date = lmp.checkversion(lammps_command)['date']\n",
    "    \n",
    "    # Define lists\n",
    "    a1vals = []\n",
    "    a2vals = []\n",
    "    E_totals = []\n",
    "    disps = []\n",
    "\n",
    "    # Loop over all shift combinations\n",
    "    for a1, a2, sfsystem in gsf_gen.iterfaultmap(num_a1=num_a1, num_a2=num_a2):\n",
    "        a1vals.append(a1)\n",
    "        a2vals.append(a2)\n",
    "\n",
    "        # Evaluate the system at the shift\n",
    "        sim_directory = Path('a%.10f-b%.10f' % (a1, a2))\n",
    "        relax = stackingfaultrelax(lammps_command, sfsystem, potential,\n",
    "                                   mpi_command=mpi_command, \n",
    "                                   sim_directory=sim_directory,\n",
    "                                   cutboxvector=cutboxvector,\n",
    "                                   etol=etol, ftol=ftol, maxiter=maxiter,\n",
    "                                   maxeval=maxeval, dmax=dmax,\n",
    "                                   lammps_date=lammps_date)\n",
    "        \n",
    "        # Extract terms\n",
    "        E_totals.append(relax['E_total'])\n",
    "        pos = relax['system'].atoms.pos\n",
    "        disps.append(pos[abovefault, cutindex].mean()\n",
    "                   - pos[~abovefault, cutindex].mean())\n",
    "    \n",
    "    E_totals = np.array(E_totals)\n",
    "    disps = np.array(disps)\n",
    "    \n",
    "    # Get zeroshift values\n",
    "    E_total_0 = E_totals[0]\n",
    "    disp_0 = disps[0]\n",
    "    \n",
    "    # Compute the stacking fault energies\n",
    "    E_gsfs = (E_totals - E_total_0) / A_fault\n",
    "    \n",
    "    # Compute the change in displacement normal to fault plane\n",
    "    delta_disps = disps - disp_0\n",
    "    \n",
    "    results_dict = {}\n",
    "    results_dict['A_fault'] = A_fault\n",
    "    results_dict['gamma'] = am.defect.GammaSurface(a1vect = gsf_gen.a1vect_uvw,\n",
    "                                                   a2vect = gsf_gen.a2vect_uvw,\n",
    "                                                   box = gsf_gen.ucell.box,\n",
    "                                                   a1 = a1vals,\n",
    "                                                   a2 = a2vals,\n",
    "                                                   E_gsf = E_gsfs,\n",
    "                                                   delta = delta_disps)\n",
    "\n",
    "    return results_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Run calculation function(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_dict = stackingfaultmap(lammps_command, ucell, potential, hkl,\n",
    "                                mpi_command = mpi_command,\n",
    "                                sizemults = sizemults,\n",
    "                                minwidth = minwidth,\n",
    "                                a1vect_uvw = a1vect_uvw,\n",
    "                                a2vect_uvw = a2vect_uvw,\n",
    "                                cutboxvector = cutboxvector,\n",
    "                                shiftindex = shiftindex,\n",
    "                                num_a1 = num_a1,\n",
    "                                num_a2 = num_a2, \n",
    "                                etol = energytolerance,\n",
    "                                ftol = forcetolerance,\n",
    "                                maxiter = maxiterations,\n",
    "                                maxeval = maxevaluations,\n",
    "                                dmax = maxatommotion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['A_fault', 'gamma'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Report results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1. Define units for outputting values\n",
    "\n",
    "- __length_unit__ is the unit of area to display delta displacemets in.\n",
    "\n",
    "- __area_unit__ is the unit of area to display fault area in.\n",
    "\n",
    "- __energy_unit__ is the unit of energy to display cohesive energies in.\n",
    "\n",
    "- __energyperarea_unit__ is the energy per area to report the surface energy in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "length_unit = 'nm'\n",
    "area_unit = 'nm^2'\n",
    "energy_unit = 'eV'\n",
    "energyperarea_unit = 'mJ/m^2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2. Print $A_{fault}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A_fault = 5.365198866947918 nm^2\n"
     ]
    }
   ],
   "source": [
    "print('A_fault =', uc.get_in_units(results_dict['A_fault'], area_unit), area_unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3. Make plots from GammaSurface object gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  -0.5  0.5]\n",
      "[ 0.5 -0.5  0. ]\n"
     ]
    }
   ],
   "source": [
    "gamma = results_dict['gamma']\n",
    "print(gamma.a1vect)\n",
    "print(gamma.a2vect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save gamma as data model\n",
    "with open('gamma.json', 'w') as f:\n",
    "    gamma.model().json(fp=f, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 846x415.692 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot with calculation a1vect, a2vect\n",
    "gamma.E_gsf_surface_plot(length_unit=length_unit,\n",
    "                         energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 846x415.692 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot with standard A1--fcc--111sf a1vect, a2vect\n",
    "gamma.E_gsf_surface_plot(a1vect=[0.5, 0.5, -1],\n",
    "                         a2vect=gamma.a2vect,\n",
    "                         length_unit=length_unit,\n",
    "                         energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nA8WHvVbiv/Z110VEwtL6XRX87dONXDwim7G9O3kdRzyUGBfNby7JZWt5FX94b73XccQDQV3mzOwnQD3w3JeXjnKbO8b1o/2aN5tZvpnll5bqwGIRCT0+n+Mnr60gOT6Gn1wwwOs4EgTG9EzlqtFdeXzeZpZs3et1HGllQVvmzOxaYApwlXPuy2JWAhy+7j4H2H6M61/hnHvEOZfnnMtLS0sLfHARkRY2s6CYRUV7+Y8LBpDaJt7rOBIkfjypP51TErh71nJq631ex5FWFJRlzswmAj8CLnTOVR320mzgCjOLN7MeQB/gC2AR0MfMephZHI2LJGa3dm4RkZZWWVPP795dT163Dlw2Muf4XyARo21CLL+aPpgNuw/y9OdFXseRVuR5mTOzF4DPgX5mVmJmNwAPAm2B981sqZk9DOCcWwW8DKwG/gF8xznX4F8scTvwLrAGeNl/r4hIWHnss82UHazhPyYP0J5y8hXjB3TmjL5pPPDhBvZW6qivSGH/N4MZefLy8lx+fr7XMUREmqTsYA1n/vZjTu+TxsNXj/Q6jgSp9bsqmPSnz/jm6K78Ytpgr+NIAJlZgXMu78jrno/MiYhI0/z5ww1U1/v44cR+XkeRINa3c1tmjOrCswu3Uri7wus40gpU5kREQsCWPZU8t3Ar3zilC73SdNKDHNud5/YlKTaa+97WyRCRQGVORCQE/O7ddcRGR/H98X28jiIhILVNPLef05uP15Xy6XptwxXuVOZERILc8pJ9vLV8Bzee3oP0lASv40iI+Na47nTtmMSv3l5NfYO2KglnKnMiIkHMOcdv3llLx+Q4bj6jp9dxJITEx0Rzz6T+rN91kJfyi4//BRKyVOZERILYpxvKmL9xD3ec05u2CbFex5EQM3FwBqN6dOQP763nQHWd13GkhajMiYgEKZ+vcVSuS8dErhrdzes4EoLMjJ9NHkh5VS1/+bjQ6zjSQlTmRESC1NsrdrBmxwF+cH4/4mL07VpOzJCcdkwfls1T84soO1jjdRxpAfruICIShHw+x18+LqR3ehum5mZ5HUdC3O3n9Kam3sdjn232Ooq0AJU5EZEg9OHa3azdWcFtZ/UiKkrHdsnJ6ZXWhim5WTzzeZGO+QpDKnMiIkHGOceDHxfSpWMiFw7VqJwExu1n96aytoEn5ml0LtyozImIBJm5hWUsK97HrWf2JiZa36YlMPpltGXioAyemF+kla1hRt8lRESCzIMfFZKRksAlI7O9jiJh5vZzelNRXc9T84q8jiIBpDInIhJE8ovKWbi5nJvO6El8TLTXcSTMDM5ux/j+6fx93mYO1tR7HUcCRGVORCSIPPhxIR2T45gxqovXUSRM3TG+D/uq6nh2wRavo0iAqMyJiASJldv28891pdxwWg+S4mK8jiNhaliX9pzepxOPfbaJQ7UNXseRAFCZExEJEg9+VEjbhBiuPlWnPUjL+u74PpQdrOX5L7Z6HUUCQGVORCQIbNhVwT9W7eRbY7uTojNYpYWd0r0jY3p25G+fbKS6TqNzoU5lTkQkCDz0z40kxkZz3bgeXkeRCHHHOX3YXVHDK4tLvI4iJ0llTkTEYzv2H2L2su1cOborHZPjvI4jEWJsr1QGZ6fwxLwinHNex5GToDInIuKxpz/fgnOOb43t7nUUiSBmxnVje1C4+yBzC8u8jiMnQWVORMRDh2obeOGLrZw/MIMuHZO8jiMRZsrQTDq1ieNJbSIc0lTmREQ89NqSbeyrquO6cd29jiIRKD4mmitHd+OjdbspKqv0Oo6cIJU5ERGPOOd4Yt5mBmWlMKpHR6/jSIT65uiuxEQZT84v8jqKnCCVORERj8wtLGPD7oNcN64HZuZ1HIlQ6SkJTB6SyayCEiqq67yOIydAZU5ExCNPzCuiU5s4pg7N9DqKRLjrxvXgYE09swq0TUkoUpkTEfHAptKDfLR2N1eN7kZ8TLTXcSTCDe3SnhFd2/PU/CJ8Pm1TEmpU5kREPPDU/CJio42rxnT1OooIAN8a14OiPVX8c/1ur6NIM6nMiYi0sv2H6phZUMLUoVmkt03wOo4IAJMGZ9A5JZ4ntE1JyFGZExFpZTPzi6mqbeB6Hd0lQSQ2Ooqrx3Tjsw1lbNhV4XUcaQaVORGRVtTgczw5v4hR3TsyOLud13FE/s2MUV2Ji4nSNiUhRmVORKQVvb96FyV7D2mTYAlKqW3imT4si1cXb2P/IW1TEipU5kREWtGzC7aQ1S6B8wZ29jqKyFF9c0w3DtU1MHvpNq+jSBOpzImItJKiskrmFpZxxaiuxETr268EpyHZ7RiYmcILXxTjnLYpCQWefzcxs8fNbLeZrTzsWkcze9/MNvg/dvBfNzN7wMwKzWy5mY047Guu9d+/wcyu9eLPIiJyLC8s2kp0lPGNU7p4HUXka5kZM0Z1YfWOA6zYtt/rONIEnpc54Elg4hHXfgx86JzrA3zofw4wCejjf9wM/BUayx9wLzAaGAXc+2UBFBEJBrX1PmbllzC+fzqdU7QdiQS3acOzSYiN4oUvir2OIk3geZlzzn0KlB9xeRrwlP/zp4Dph11/2jVaALQ3s0xgAvC+c67cObcXeJ+vFkQREc+8t3oneyprmTFamwRL8EtJiGXykCxmL91GZU2913HkODwvc1+js3NuB4D/Y7r/ejZw+I8JJf5rX3f9K8zsZjPLN7P80tLSgAcXETma5xduJbt9Imf0SfM6ikiTzBjVhcraBt5avt3rKHIcwVrmvo4d5Zo7xvWvXnTuEedcnnMuLy1N31RFpOVtLqtk/sY9zBjVheioo327Egk+I7t1oHd6G021hoBgLXO7/NOn+D9+eVBcCXD4O4dzgO3HuC4i4rkXvthKTJRxeZ4WPkjoMDOuOKULS4v3sXbnAa/jyDEEa5mbDXy5IvVa4I3Drl/jX9U6Btjvn4Z9FzjfzDr4Fz6c778mIuKpmvoGZhWUcO6AzqRr4YOEmItH5BAXHcWLGp0Las0qc2bWz8wmmdnFZna6mbU52QBm9gLwOdDPzErM7AbgN8B5ZrYBOM//HGAOsAkoBB4FbgNwzpUDvwQW+R//5b8mIuKpd1ftolwLHyREdUyOY8LgDF5dXEJ1XYPXceRrxBzvBjPrTmNp+ibQmX9/f1q9mc0FHgZmuhPYXdA5N+NrXhp/lHsd8J2v+XUeBx5v7u8vItKSnl+4hS4dEzm9dyevo4ickBmndOHNZdt5Z+UOLhqe43UcOYpjjsyZ2e+AlUA/4D+AwUA7IB7IBC4A5tM4crb08E18RUQi3cbSgyzYVM4Vp3QlSgsfJESN6ZlKt9QkLYQIYscbmWsL9HXOHW0xwS7/4wPgp2Z2GTAAWBzYiCIioelF/8KHy/I0miGhK8p/aslv/7GOjaUH6ZV20u+wkgA75sicc+7bX1PkjnbvTOfcc4GJJSIS2r5c+HDewM6kt9XCBwltl47MISbKeGmRRueCUbCuZhURCWnvr97F3qo6ZozSwgcJfeltEzinfzqvLt5GfYPP6zhyhCaVOTM71cx+Z2Y/MrOsI15LMTMtPBAROcysghKy2iUwTgsfJExcMjKHsoM1fFZY5nUUOcJxy5yZTQA+pfH8028DK83s7MNuSeT/9oQTEYl4uw5U8+n6Ui4ekaMTHyRsnN0vnfZJsby6eJvXUeQITRmZ+ynwR+dcLtAT+APw5hGFTkRE/F5bsg2faxzJEAkXcTFRXDg0i/dW7eRAdZ3XceQwTSlzg4FHoHGfN+fcr4C7gdlmdlpLhhMRCTXOOWYVlJDXrQM9OiV7HUckoC4ekUNNvY85y3d4HUUO05Qy52icSv2/C849BPwCeBsY3QK5RERC0rKS/RTuPsilGpWTMDQ0px290pI11RpkmlLm1gKnHHnROfd74E/Ai4EOJSISqmYVFJMQG8UFuZleRxEJODPj4hE5fFFUztY9VV7HEb+mlLnXgIuP9oJz7uc0TsHqHb4iEvGq6xqYvXQ7EwdlkJIQ63UckRZx0fBszODVJSVeRxG/45Y559zvnHOTj/H6951z2q9ORCLeB2t2caC6nktHdvE6ikiLyWqfyNheqby6eBsncCS7tACVMBGRAPlyb7lTe6V6HUWkRV08PIet5VUUbNnrdRRBZU5EJCC0t5xEkomDM0iKi+aVxZpqDQZNLnNm1qslg4iIhDLtLSeRJDk+homDM3hr+Q6q6xq8jhPxmnqc1yDgny0bRUQkNDnnmJlfrL3lJKJcMiKHiup6Plizy+soEa8px3mNAD4G/qfl44iIhJ6lxfvYWFqpveUkoozpmUpmuwReKdBUq9eOWebMbBzwIfBX59yDrRNJRCS0zCooISE2isnaW04iSHSUcdHwbD7dUMbuimqv40S0443M/QN4zjl3b2uEEREJNdV1Dcxe1ri3XFvtLScR5uIR2TT4HG8u0/FeXjpemWsA2rRGEBGRUPTuqp1UVNdzWZ72lpPI0zu9LYOyUpi9bLvXUSLa8crc2cAFZva/rRFGRCTUzCooIbt9Iqf21N5yEpmmDctiWfE+isoqvY4SsY5Z5pxzS4CzgCvM7J5WSSQiEiK27zvE3MIyLhmZQ5T2lpMINXVoFmZodM5DTTnOazVwBnBzy8cREQkdry4uwTm4TKtYJYJltkvklO4deWOpjvfySpP2mXPObQROb+EsIiIhwznHrIISxvTsSJeOSV7HEfHUtGFZbCytZPWOA15HiUhNPgHCOaeNZERE/BYV7aVoTxWXjdTCB5ELBmcSE2XMXqqpVi/obFYRkRMwM7+YNvExTBqS4XUUEc91SI7jzL5pzF62HZ9PU62tLaY5N5vZBGA8kM4RRdA5d00Ac4mIBK3KmnreXrGDqblZJMU169uoSNi6cFgWH67dzaKickZrdXeravLInJn9CngHOB/IANKOeIiIRIQ5K3ZQVdvAZXla+CDypfMGdiYxNpo3tKq11TXnR8qbgW85555uqTAiIqFgZkEJPTolM7JbB6+jiASNpLgYzhvYmTkrdvCfUwcRF6N3crWW5vyX9gHzWyqIiEgo2LKnki82l3PpyBzMtLecyOGmDctiX1UdcwtLvY4SUZpT5h4CbmypICIioWBWQQlR1ngmpYj8u9P7pNE+KZY3tKq1VTVnmvWXwFtmtgxYDtQd/qJz7vpABhMRCTYNPscrBSWc1ieNzHaJXscRCTpxMVFcMCST1xZvo6q2XguEWklzRub+C5gERAOZQJcjHiIiYW3+xjK276/WiQ8ixzBtaBaH6hp4f/Uur6NEjOZU5tuB651zT7ZQFhGRoDYzv4R2ibGcN7Cz11FEgtYp3TuS2S6B2Uu3M22Y3o7QGpozMlcLzG2pIEdjZnea2SozW2lmL5hZgpn1MLOFZrbBzF4yszj/vfH+54X+17u3ZlYRCW/7q+r4x6qdTBuWRUJstNdxRIJWVJQxdWgWn6wvZV9VrddxIkJzytwjwA0tFeRIZpYNfBfIc84NpnF69wrgf4D7nXN9gL2HZboB2Ouc6w3c779PRCQgZi/bRm29j8vz9K4SkeOZmptFvc/x7qqdXkeJCM0pc5nAt81ssZk9YWaPHP5ooXwxQKKZxQBJwA7gHGCW//WngOn+z6f5n+N/fbxp3wARCZCX80sYkJnCoKwUr6OIBL3B2Sl0S03izWU7vI4SEZpT5noBS4H9QHegz2GP3oEO5pzbBvwe2EpjidsPFAD7nHP1/ttKgC8n5LOBYv/X1vvv/8p5ImZ2s5nlm1l+aan2wRGR41uz4wArtu3n8jztLSfSFGbGlNxM5m8so+xgjddxwt5xy5yZzTKzK4Hpzrmzv+ZxTqCDmVkHGkfbegBZQDKNq2mP9OWJvkf7DvuV036dc4845/Kcc3lpaTqFTESOb2Z+CXHRUUzXm7lFmmzq0Cx8Dt5ZqanWltaUkblVwI+A3Wb2rpndYmYZLZwL4Fxgs3Ou1DlXB7wKjAXa+6ddAXKAL3cmLMG/RYr/9XZAeSvkFJEwVlvv47UlJZw3sDMdkuO8jiMSMvp1bkvv9Da8pbNaW9xxy5xz7l7n3FBgIPA+cA1QbGbzzeyHZhbwKVa/rcAYM0vyv/dtPLAa+Bi41H/PtcAb/s9n+5/jf/0j59xXRuZERJrjwzW72HcJggkAACAASURBVFtVx2V52ltOpDm+nGr9oqicXQeqvY4T1pr8njnn3Ebn3O+dc+NoHAF7GjgPWG1mK8zsv8ysZ6CCOecW0riQYTGwwp/1ERpHCe8ys0Ia3xP3d/+X/B1I9V+/C/hxoLKISOR6Ob+YjJQETu+jt2WINNeU3Cycg7eXayFES2rOAoh/cc7tdM497Jw7H0gHfgsMBi4OZDj/qGB/59xg59zVzrka59wm59wo51xv59xlzrka/73V/ue9/a9vCmQWEYk8O/dX88n6Ui4ZmU10lBY+iDRX7/Q2DMhM4a3lmmptSSdU5g7nnNvnnHvGOXexc+73gQglIhIMXllcgs/BZSO1t5zIiZqSm8nirfso2VvldZSwddJlTkQkHDnnmJlfzKgeHeneKdnrOCIha2puFqCp1pZ03LNZzey9Jvw69TTuBfce8LIWHohIqMvfspeiPVXcfk4fr6OIhLSuqUkMzWnHW8t3cMuZvbyOE5aaMjK3rQmPUhq3CXkS+GVLBBURaU0vLyomOS6aC4a0xk5MIuFtSm4WK7btp6is0usoYem4I3POueua+ouZ2TTgz8BPTyaUiIiXDtbU8/aKHVw4NIukuON+mxSR45icm8l9c9bw1vLtGu1uAYF+z9xcoDDAv6aISKuas3wHVbUN2ltOJECy2ieS160Db+l9cy2iWT9ymtkEGjfvTeeIIuicu8Y5twcI+NFeIiKt6eX8YnqmJTOiawevo4iEjSm5mfznm6vZsKuCPp3beh0nrDR5ZM7MfgW8A5wPZABpRzxERELeptKD5G/Zy+V5XWg8fEZEAuGCIZmYwZsanQu45ozM3Qx8yzn3dEuFERHx2syCEqKjjIuHZ3sdRSSspKckMLpHR95evp07z+2jH5YCqDnvmfMB81sqiIiI1+obfLxSUMJZfdNIT0nwOo5I2JmSm8XG0krW7arwOkpYaU6Zewi4saWCiIh47bMNZeyuqOGyPJ34INISJg7OIMq0gXCgNWea9ZfAW2a2DFgO1B3+onPu+kAGExFpbS/nF5OaHMc5/dO9jiISljq1iefUXqm8vXwHd53XV1OtAdKckbn/AiYB0UAm0OWIh4hIyCqvrOWDNbu4aHg2cTE66VCkpUweksWmskrW7NBUa6A05zvW7cD1zrnBzrlznXPnHf5oqYAiIq3h9SXbqGtwmmIVaWETBnUmOsp4e8V2r6OEjeaUuVoaNwUWEQkrzjlezi9maE47+mVo/yuRlpTaJp6x/qlWHeUeGM0pc48AN7RUEBERr6zcdoC1Oys0KifSSiYPyaRoTxWrth/wOkpYaM4CiEzgEv8pEMv46gKImwMZTESktcwsKCY+JoqpQ7O8jiISESYMyuAnr6/kreU7GJzdzus4Ia85I3O9gKXAfqA70OewR++AJxMRaQXVdQ28vmQbEwdn0C4x1us4IhGhQ3Ic43p34u0V2zXVGgBNHplzzp3dkkFERLzw3updHKiu53JNsYq0qim5mdw9azkrtu0nN6e913FC2jFH5sxsZFN/ITNLMLMBJx9JRKT1zMwvJrt9Iqf2TPU6ikhEmTAwg9ho0wbCAXC8adY3zOw1M5tgZke918yyzeweYAMwLuAJRURaSMneKuYWlnFZXg5RUdq8VKQ1tUuK5bTenXhLq1pP2vHKXD9gJfAssN/MPjWzF8zsCTN7w8wKga3AucAM59xjLZxXRCRgZhWUAHDpyByPk4hEpsm5WWzbd4hlJfu9jhLSjlnmnHOVzrmfATnA1UA+kEDjytYDwF+AQc658c457UEnIiHD53PMzC/htN6dyOmQ5HUckYh03sDO/qlWbSB8Mpq0AMI5VwO87n+IiIS8+Rv3sG3fIX48qb/XUUQiVrvEWM7ok8bby3fwHxcM0FmtJ0gHEIpIRHopv5h2ibGcN7Cz11FEItrk3Ey2769m8dZ9XkcJWSpzIhJx9lXV8u6qnVw0PJuE2Giv44hEtHMHdiYuOoo5K7Sq9USpzIlIxHlj6XZq631clqeFDyJeS0mI5Yy+acxZsQOfT6taT4TKnIhEnJcWFTM4O4VBWTpGSCQYTMnNZMf+apYU7/U6Skhqdpkzs7/6P+q7oIiEnJXb9rN6xwGd+CASRMYPSCcuJoq3l+/0OkpIOpGRuVP9Hz8OZBARkdbwcn4xcTFRTBua7XUUEfFrmxDLmZpqPWEnUubeN7PFQIaZ3Wxmo81MmzSJSNCrrmvg9SXbmDQ4g3ZJsV7HEZHDTMnNZOeBahZv1VRrczW7zDnnfghMB+po3Dz4bmCpma0zs1cCnE9EJGDeXbWTA9X1mmIVCULjB3QmLiaKt3RWa7M1adPgIznntprZeOdc4ZfXzCwZGBKwZCIiAfZyfjE5HRI5tWeq11FE5Aht4mM4q28a76zcwc+nDNR5yc1wMqtZnzezFAAz+z5wB7A0IKlERAKsuLyKeYV7uGxkF/0jIRKkJudmsutADQWaam2Wkylz8c65A2Y2CrgSiAYeDUysRmbW3sxmmdlaM1tjZqeaWUcze9/MNvg/dvDfa2b2gJkVmtlyMxsRyCwiEtpm5hdjBpdqbzmRoDV+QGfiY6J4W1OtzXIyZa7ezOKAq4HfO+fuAwJ9yOGfgH845/oDQ4E1wI+BD51zfYAP/c8BJgF9/I+bgb8GOIuIhKj6Bh8v55dwZt80stsneh1HRL5Gm/gYzu6XzpwVO2jQqtYmO5ky9wCwApgCvOm/1vakE/n5p3DPAP4O4Jyrdc7tA6YBT/lve4rGxRj4rz/tGi0A2ptZZqDyiEjo+ue6UnYeqOaKU7p6HUVEjuOC3Ex2V9SQX1TudZSQccJlzjn3FDASGOScO2RmfYDPA5YMegKlwBNmtsTMHvMvsujsnNvhz7ADSPffnw0UH/b1Jf5r/8a/nUq+meWXlpYGMK6IBKsXF22lU5t4xg9IP/7NIuKp8f3TiY/RWa3NccJlzsyKgVeAe81sBhDjnLsuYMkaV9qOAP7qnBsOVPJ/U6pHjXSUa18Zo3XOPeKcy3PO5aWlpQUmqYgErZ37q/lo7W4uy8shNlonGIoEu+T4GM7pn86clTs11dpEJzvNugrYAIwGFpjZKjP7rZnFByBbCVDinFvofz6LxnK368vpU//H3Yfdf/jmUTnA9gDkEJEQNjO/GJ+DK07R3nIioWJybialFTUs0lRrk5xMmbvSOXeXc+4x59z3gXHARzSWqvtPNphzbidQbGb9/JfGA6uB2cC1/mvXAm/4P58NXONf1ToG2P/ldKyIRCafz/FSfjFje6XSLTXZ6zgi0kTn9E8nIVarWpvqZMrcfjPL+/KJc24lMNo59wAw5qSTNboDeM7MlgPDgF8DvwHOM7MNwHn+5wBzgE1AIY1bpNwWoAwiEqLmFpZRsvcQM0Zp4YNIKEmKa5xqfWelVrU2xQmdAOF3E41Fay2wBOhH4xFfAHEnGwzAObcUyDvKS+OPcq8DvhOI31dEwsOLi7bSISmW8wd19jqKiDTTlNws5qzYycLNexjbq5PXcYLayaxm3UDjCNxrQAqwEpjiX3H6QmDiiYicmNKKGt5btYtLRuQQHxPtdRwRaaaz+6WTFBets1qb4Lhlzsz2mVnHo73mnPM5515zzv3COfegc26vc67Sv4GwiIhnXllcQr3PccUoLXwQCUWJcdGMH9CZf6zcSX2Dz+s4Qa0pI3MpHDYd6z8uK+uw5wlmdnZLhBMRORHOOV5aVMwp3TvQOz1ge5mLSCubPCST8spaPt+0x+soQe1Eplk7AwmHPW8HfBCYOCIiJ2/BpnI2l1XqxAeREHdWvzSS46J5a5mmWo8lUDtoHm3DXhERT7y4aCttE2K4YIhO9BMJZQmx0Zw3sDP/WLWTOk21fq1AlTmtGxaRoLC3spZ3Vu7kouHZJMZp4YNIqJuSm8X+Q3XMLSzzOkrQamqZ+5GZTTezLjQWN5U3EQlKMwuKqa33cdXobl5HEZEAOL1vJ9omxGiq9Riass/cK8BFwJ00ljgDHjGzz4ECdGSWiAQJn8/x3MKtjOrekX4ZWvggEg7iY6I5f2AG763eSU39YG01dBTHHZlzzl3mnOsJdAIuAH5G46H319O4x9zCY3y5iEir+aywjC17qrhqjBY+iISTKbmZVFTX89l6TbUeTZNPgHDOlQPv+h8AmFkGMAoYGfhoIiLN88znW+jUJo6JgzO8jiIiATSudyfaJcby9oodnDtQJ7oc6aQWQDjndjrnZjvn7g1UIBGRE7Ft3yE+WruLy/O6aBpGJMzExUQxYVBn3l+9i+q6Bq/jBJ1ArWYVEfHUi19sxQEzRmmKVSQcTcnN4mBNPf9cV+p1lKCjMiciIa+23scLXxRzTr90unRM8jqOiLSAU3ul0iGpcapV/p3KnIiEvPdW76TsYA3fHKPtSETCVWx0FBMHZ/Lhml0cqtVU6+FU5kQk5D3z+RZyOiRyRt80r6OISAuamptJVW0DH63d7XWUoKIyJyIhbcOuChZuLueq0d2IjtLJgiLhbHTPVDq1ieet5dri9nAqcyIS0p5buJW46Cguz8vxOoqItLDoKGPykAw+XLubiuo6r+MEDZU5EQlZlTX1vFJQwqQhGaS2ifc6joi0gguHZVFb7+O9Vbu8jhI0VOZEJGTNXradipp6rtbCB5GIMaJrB7LbJzJ7maZav6QyJyIhyTnHU/OL6J/RlpHdOngdR0RaiZkxdWgWcwvL2HOwxus4QUFlTkRC0ueb9rB2ZwXXjeuOmRY+iESSC4dm0eBzzFm50+soQUFlTkRC0hPziuiQFMu0YdleRxGRVjYgsy2909vwpqZaAZU5EQlBW/dU8cGaXVw5uisJsTqHVSTSmBlTc7NYVFTOjv2HvI7jOZU5EQk5T31eRLQZV4/p7nUUEfHIhcOycA7eWqbjvVTmRCSkHKyp5+VFxUwakklGuwSv44iIR3p0SmZIdjutakVlTkRCzCsFJVTU1HPduO5eRxERj104NIsV2/azuazS6yieUpkTkZDh8zmenF/EsC7tGdFV25GIRLopQzMxg9lLI3t0TmVORELGJ+tL2VxWqVE5EQEgs10ip3TvyOxl23DOeR3HMypzIhIyHp+3mc4p8VwwJNPrKCISJC4cmsXG0krW7KjwOopnVOZEJCQU7q7gsw1lXD2mG7HR+tYlIo0uGJJJTJRF9EIIfUcUkZDwxLwi4mKimDGqq9dRRCSIdEyO47Q+nXhz2faInWpVmRORoLe/qo5XF29j+rAsUtvEex1HRILMhUOz2LbvEPlb9nodxRMqcyIS9J5duIVDdQ1cN66H11FEJAhNGJRBYmw0ry3Z5nUUT6jMiUhQq65r4Il5RZzRN40BmSlexxGRIJQcH8OEQZ15e/kOauobvI7T6oK6zJlZtJktMbO3/M97mNlCM9tgZi+ZWZz/erz/eaH/9e5e5haRwHltyTbKDtbw7TN7eh1FRILY9OHZ7D9Ux8drd3sdpdUFdZkDvgesOez5/wD3O+f6AHuBG/zXbwD2Oud6A/f77xORENfgczz66SZyc9pxas9Ur+OISBA7rXcnOrWJj8ip1qAtc2aWA0wGHvM/N+AcYJb/lqeA6f7Pp/mf4399vP9+EQlh76/exaaySm45oxf6Ky0ixxITHcW0YVl8tHY3+6pqvY7TqoK2zAF/BO4GfP7nqcA+51y9/3kJkO3/PBsoBvC/vt9//1eY2c1mlm9m+aWlpS2VXUROknOOhz/ZSNeOSUwcnOF1HBEJARcNz6auwfHW8h1eR2lVQVnmzGwKsNs5V3D45aPc6prw2r9fdO4R51yecy4vLS3tJJOKSEtZVLSXpcX7uOmMnkRHaVRORI5vUFYKfdLbRNxUa1CWOWAccKGZFQEv0ji9+kegvZnF+O/JAb7c7rkE6ALgf70dUN6agUUksB7+ZCOpyXFcNjLH6ygiEiLMjItGZFOwZS9b91R5HafVBGWZc87d45zLcc51B64APnLOXQV8DFzqv+1a4A3/57P9z/G//pGL1G2gRcLAup0VfLR2N9eO7U5CbLTXcUQkhEwflo0ZETU6F5Rl7hh+BNxlZoU0vifu7/7rfwdS/dfvAn7sUT4RCYBHPt1EYmw0V4/p5nUUEQkxWe0TGdMjldeWlETM8V5BX+acc/90zk3xf77JOTfKOdfbOXeZc67Gf73a/7y3//VN3qYWkRO1Y/8h3li6jW+c0oUOyXFexxGREHTR8GyK9lSxpHif11FaRczxbxE5Oc45issPsXzbPpaX7Gd5yT7W7awgJjqKtvExtEmIoW1CDG3iY8hISeD0PmmM7Z1KUpz+94xEj8/djANuPF1Hd4nIiZk0JIOfvbGS15dsY0TXDl7HaXH611JazOKte3l87mbmFZaxt6oOgLjoKAZkpTBxcCbgqKiup6K6noM19ZRVVPHp+jKe+nwLcdFRjOrRkTP7pnFWvzR6p7fRPmMRoLyylucXbmVKbiY5HZK8jiMiIaptQiznDezMm8u289PJA4mLCfqJyJOiMicB1eBzvL96F499ton8LXtJSYhh4uAMhnZpT252e/pltD3mX6qa+gYWbd7LJ+t38891pdw3Zw33zVnDwMwUbj6jJ5NzM4mNDu+/lJHssc82UVXXwO1n9/Y6ioiEuItHZPPW8h18sr6U8wZ29jpOi7JIeXPg0eTl5bn8/HyvY4SF6roGZhaU8PfPNlG0p4qcDonccFoPLs/rQnL8if/MULK3io/W7uap+UVsLK0kq10C15/Wg2+c0oW2CbEB/BOI1/ZW1nLa/3zE2f3TefDKEV7HEZEQV9fgY8yvP2R0z448dNVIr+MEhJkVOOfyjryukTk5aYuKyvnRK8vZVFrJ0Jx2/OXKEUwY1JmYAIyg5XRI4ppTu/PN0d34eN1uHvl0E796ew1/+nADV47uyq1n9qJ9kt4kHw4em9s4Kvfd8X28jiIiYSA2OooLh2Xx3IKt7K2sDesFVSpzcsIqquv47T/W8cyCLeR0SOTJ607hzL5pLfLetqgoY/yAzowf0Jmlxft49LNNPPrpJl5aVMyd5/blqtFdA1IexRt7K2t5av4WLhiSSd/Obb2OIyJh4rKRXXhiXhFvLN3Gt8aF76Iq/esnJ+TjtbuZcP+nPLtwC9eP68G73z+Ds/qlt8oihWFd2vOXK0cw53unMygrhXtnr2LSnz7j0/U6azdU/X3uZg7W1PPdczQqJyKBMzArhSHZ7Xg5v8TrKC1KZU6apbKmnjtfWsp1Ty4iOT6GV24dy8+nDjyp98WdqP4ZKTx7w2geuXoktQ0+rnn8C254chGbSg+2ehY5cfuqanlyfhGTh2TSL0OjciISWJfn5bB6xwFWbtvvdZQWozInTVayt4pL/jqfN5Zu47vj+/DWd0/zfP8eM+P8QRm8d+cZ3DOpPws3lzPxT5/x97mb8fkid3FPKPlyVO6O8VrBKiKBd+HQbOJiopiZX+x1lBajMidNkl9UzrQH57Ft3yGevG4Ud53Xl/iY4DkzMz4mmlvO7MVHPziTM/p04pdvrebKxxZQsjdyDloORfuqanlyXhEXDMmgf0aK13FEJAy1S4pl4qAMXl+6neq6Bq/jtAiVOTmuWQUlXPnoQtomxPDabeM4o2+a15G+VnrbBB69Jo/fXprLym0HmPjHz5iZXxwx5/OFmsfnbqaipl4rWEWkRV2e14X9h+p4f/Uur6O0CJU5+VoNPsd/v7OGH8xcRl73Drz+nXH0Tm/jdazjMjMuz+vCO/4FEj+ctZybnylgz8Ear6PJYfZX1fHEvCImDdaonIi0rLG9Uslun8jLYTrVqjInR1XX4OP25xfzt082cdXorjx1/aiQ28+tS8ckXrhpDD+dPIBP1pcy9c9zWRYhhy6HgsfmbtKonIi0iqgo49KROcwtLGPbvkNexwk4lTn5iroGH3c8v4R3Vu7kp5MHcN9FQ0L2CK2oKOPG03vy6q1jMTMu+9vnYfuTWSjZdaCaxz7bzJTcTAZkalRORFrepSNzcA5eKQi/bUpC819oaTF1DT6+9+IS/rFqJz+bMpAbT+/pdaSAGJzdjjfvOI1Tunfg7lnL+dnrK6mt93kdK2L94b311Pt83D2hv9dRRCRCdOmYxLjeqcwsKA673Q5U5uRf6ht8fP+lpcxZ0Tgid8Np4bVbdsfkOJ66bhS3nNGTZxZs4cpHF7D7QLXXsSLOup0VzCwo5ppTu9M1NcnrOCISQS7P60Jx+SEWbN7jdZSAUpkToLHI3fnyMt5evoOfXDAgbEbkjhQTHcU9FwzgzzOGs2r7Aab8eS4rSsJ3I8lg9N/vrKFNfAx3nKN95USkdU0YlEHbhBhmhtmJECpzQn2Dj7teXsaby7Zzz6T+3HRGeBa5w00dmsVr3xlLbHQUl//tcz5cE57L1YPN3A1l/HNdKbef0zvkFtSISOhLiI1m2rAs5qzYwYHqOq/jBIzKXIRzznHv7FXMXradH03szy1n9vI6Uqvpn5HCa98ZS+/0Ntz0dD7PfF7kdaSw5vM5fj1nDdntE7nm1O5exxGRCHV5Xhdq6n28sXS711ECRmUuwv3t0008t3Ar3z6zF7eeFTlF7kvpbRN46ZYxnNM/nZ+9sYpfz1kTdm+MDRavLdnG6h0HuHtiPxJig+f0EBGJLEOy2zEoK4XnFmwJmw3lVeYi2JvLtvObd9YydWgWd0/o53UczyTFxfC3q/O49tRuPPLpJm5/YXHYHvnileq6Bv73vXUMyW7H1Nwsr+OISAQzM745phtrd1ZQsGWv13ECQmUuQn2xuZz/9/IyTunegd9dmktUlHkdyVPRUcZ/XjiIn04ewDsrd3Llowsor6z1OlbYeHzeZrbvr+Y/LhgQ8f+viYj3pg3Lom18DM8u2OJ1lIBQmYtAG0sPctPT+eR0SOSRq/M05eVn1rjB8ENXjmDV9gNc8tf5bNlT6XWskFd2sIa/fryRcwekc2qvVK/jiIiQFBfDxSOymbNiZ1gc9agyF2HKDtbwrSe+ICbKePK6UXRI1orCI00aksnzN41mX1UtFz80nyVbw2MY3iu/ems11fUN/HjSAK+jiIj8y1VjulHb4OPlMNimRGUuglTXNXDjU/mUVtTw2LV52rD1GEZ268grt44lOT6GGY8u4N1VO72OFJI+21DK60u3c+uZveid3sbrOCIi/9K3c1tG9+jI819sCfmFbypzEcI5x49fWc7S4n388RvDGN61g9eRgl7PtDa8ettY+mWk8O1nC3hy3mavI4WUQ7UN/OS1lfTslMxtZ2uDYBEJPt8c043i8kN8sqHU6ygnRWUuQjz8ySZeX7qdH5zfl4mDM72OEzI6tYnnxZvGcO6Azvznm6v5xZurqG/Qma5N8cBHG9haXsWvLhqs92WKSFCaMCiDTm3ieS7EF0KozEWAD9fs4rfvrmVKbibf0QhJsyXGRfPwN0dy/bgePDGviBufzg+rncNbwtqdB3j0001cOjKHsb06eR1HROSo4mKi+MYpOXy0djcle6u8jnPCVObC3IZdFXzvxaUMykrhd5cOxUzbQpyI6Cjj51MHct9Fg5m7oYxLHprP1j2h+xe/Jfl8jnteXUFKYiw/uUCLHkQkuM0Y1RUHvPDFVq+jnDCVuTC2r6qWG5/OJyE2mkeuziMxTlNdJ+uq0d14+vpR7K6oYfpD8/hic7nXkYLOc19sZcnWffx08gCtlhaRoJfTIYnx/dN5aVExtfWh+TYalbkwVdfg4zvPL2bHvmr+dvVIstoneh0pbIzt3YnXbhtL+8RYrnpsATPzi72OFDR2Hajmt++sZVzvVC4anu11HBGRJrlqTDfKDtaG7M4FKnNh6r631zCvcA+/vngII7tp5Wqg9Uxrw2u3jWNUj478cNZyfvXW6ohfGOGc4+dvrKS2wcd904doSl9EQsaZfdLo0jExZE+EUJkLQ88t3MKT84u48bQeXDoyx+s4YatdUixPXjeKa0/txmNzN3Pdk4vYXxW5CyOeW7iVd1ft4q7z+tK9U7LXcUREmiwqyrhqdDcWbi5n9fYDXsdptqAtc2bWxcw+NrM1ZrbKzL7nv97RzN43sw3+jx38183MHjCzQjNbbmYjvP0TeGP+xjLufWMVZ/VL4x69+bzFxUZH8Ytpg/nNxUNYsGkP0x+aR+HuCq9jtbpV2/fzX2+t5sy+adx0ek+v44iINNuMU7qSFBfNY3M3eR2l2YK2zAH1wP9zzg0AxgDfMbOBwI+BD51zfYAP/c8BJgF9/I+bgb+2fmRvbdlTyW3PLaZ7p2QemDGcaB1o3mquGNWVF24aQ0V1HdP/Mp8P1+zyOlKrOVhTzx3PL6FDUix/uHwoUfr/TkRCULukWC7P68LspdvZsf+Q13GaJWjLnHNuh3Nusf/zCmANkA1MA57y3/YUMN3/+TTgaddoAdDezCJmd9yK6jpueCofgMeuySMlIdbjRJEnr3tHZt9+Gt07JXHj0/n85ePCkD8i5nicc/z0tRUU7ankT1cMJ7VNvNeRRERO2A2n9cDnHE/OL/I6SrMEbZk7nJl1B4YDC4HOzrkd0Fj4gHT/bdnA4csKS/zXwl6Dz/HdF5ZQVFbJQ1eN0PuVPJTVPpGZt4xlam4Wv3t3Hbc8WxDWGwzP/P/t3XmcFNW5//HPM8PAMMgOsu+LyiIIo7KIS9QrElCjxF3RiAto1Ou9F6PGyM8txkS9ajQKSjRoXK6KEsUFjSiILMMiO8gqwy4g+4Az8/z+6JqkbWZgmKXX7/v16hfd1VV9nnO6mHr61KlTObm8O3c9t5/VkV5t68c6HBGRcmlRL4tzuzbh79O/Y/f+/FiHU2pxn8yZ2VHA28Dt7n6oUYnFnds5qFvEzG4wsxwzy9myJbHvxVbkkQ8X8/nSLYw8r7Nm248D1aum8+Sl3blvUCc+X7KZ856ewpKNiTeg9nCWbdrF78YvoE+7+rqziIgkjev7tWVXXj5vzEycaafiOpkzSOdWdgAAF/9JREFUswxCidyr7v5OsHhT0enT4N/NwfJcoEXY5s2B9ZGf6e6j3D3b3bMbNmxYecFHyZs5axk9eRVX927Flb1axTocCZgZ1/Ztw2s39GLPgQJ+8cxU3pu7LtZhVZh9Bwq4+dXZHFWtCv97SXeNzxSRpNG9RR1Oal2PMVNWJcyUU3GbzFlokqoXgcXu/njYW+OBIcHzIcB7YcuvDq5q7QXsKDodm6xmrNrGPePm07d9fe4d2CnW4UgxTmxdjw9+fQpdmtXittfnMnL8woSdYbxIfkEht70+h+VbdvPEJd05ulZmrEMSEalQ15/alnU/7OPDBYkxiXDcJnNAX+Aq4GdmNjd4DAAeAc42s2+Bs4PXABOAlcByYDQwPAYxR83abXu56ZVZNK+bxbOX9yQjPZ6/ytR2dK1M/n59L67t25qXpq7mor9MZeWW3bEOq0wKC50Rb83jk0WbGDmoM/06JH7vtohIpDOPPZq2DWowevJK3OP/Qra4zQDcfYq7m7sf7+7dg8cEd9/q7me6e4fg323B+u7uN7t7O3fv6u45sa5DZdm9P5+hL+eQX1DIC0OyqZ2lK1fjXUZ6GvcN6sxzV/bgu217Gfj0FN6cuTYh/kgUcXfuG7+Qd+as47//oyND+rSOdUgiIpUiLc24rl8b5uXuSIh7cMdtMifFKyh0bg9OcT1zRQ/aNTwq1iHJEejfpQkf3d6P45vXZsTb87jltTkJc9eIP32ylLHT1nDjqW11wYOIJL2LejSnXo2qjJ4c/5MIK5lLMI9+vIRPF2/mdwM76RRXgmpSuzqvDu3FiP7H8PGCjZz75JdMX7k11mEd0nNfrOCZz1dw2Ukt+c25x+q+qyKS9DIz0rmqVys+XbyZFXE+NEbJXAJ5e1Yuz3+xkitObsnVvXXlaiJLTzOGn96et4f1oWqVNC4ZNY0735rHtj0HYh3aQV6ZtoZHPlzCoG5NefCCLkrkRCRlXNW7FdWqpDHqi/junVMylyBmrt7GXe/Mp0+7+ow8r7MOqEmiW4s6fHBrP248tS1vz87ljD9N4tXpayiIgztHHMgv5OEJi/ntuwv42bFH8/jF3TQFiYiklAZHVeOyk1ry9uxc1mzdE+twSqRkLgGs3baXG8fOolnd6jx7RQ9duZpkalSrwl0DjmPCbf04rklN7hm3gF88+xXfrP0hZjGt3baXi5//mlFfruTq3q2034lIyhp+ejuqpBtPfvZtrEMpkf46x7nQPVdnkl9QyItDsqmTVTXWIUkl6dioJq9d34snL+3Oxh15XPDsV9z++hwWrt8R1Tg+WrCBAU9NZsWW3fzlih7cf34XMjPSoxqDiEi8OLpWJkN6t+bdOetYvnlXrMMplpK5OFZ0z9UVW/bwlyt70lZXriY9M+P87s347L9O44Z+bZm4aBM/f2oKV74wnS+WbanUqUzyfizgvvcWcNMrs2nboAYTbu3HuV2bVFp5IiKJ4sbT2pFVtQpPTIzP3rkqsQ5ASvbwhNA9Vx+8oAt92+ueq6mkZmYGdw04juFntOe1Gd/x169WMWTMDI5pVJOh/drQv0tjamZWzPyCP+w9wJs5a/nb12vI3b6Poae0YUT/Y6laRb/1REQA6tWoyq9OacNTn33LsHU76NKsdqxD+glLpElLK1p2drbn5MTn3MKvzfiOu96ZzzV9WjPyvM6xDkdi7EB+If/4Zj2jJ69kycZdVE1Po3e7+pzdqRFnd2pEozLcUmvJxp28PHU14+asI+/HQk5qU49bzmjPqR015Y2ISKSdeT/S7w+fk92qLi9ec2JMYjCzWe6efdByJXPxl8xNXfE9V784gz7tGzBmSDZVNPBcAu5OzprtfLJwI58s2sSarXuB0FWxfdrVp2W9LJrVqU7zutVpWqc6mRnpFBY6G3fmsWbrXtZu28uabXuYuXo7M1ZtIzMjjQu6N+Pq3q3p1LRWjGsnIhLfnp20nEc/Wso7w/vQo2XdqJevZK4Y8ZjMLdu0i4v+MpXGtTJ5e3gfalXQqTRJPu7Ot5t3M3HRJj5ZuJH563YQOaNJ/RpV2bU/nwP5hf9alp5mtGlQg1/2bM4lJ7bQRTUiIqW090A+pz76Occ0rsmrQ3tFvfySkjmNmYsjm3fmce1fZ5KZkc5frz1RiZwckpnRsVFNOjaqyc1ntCe/oJCNO/NYt30fudv3se6HfWzYsY9amRm0qJdFq/pZtKpXg6Z1MtXbKyJSBllVqzDs9PY88P4ipq74nj7t4mM8u5K5OLFnfz6/enkm2/ce4I0betO8blasQ5IEUyU9jeZ1s2heN4uTYx2MiEiSuuLkloz+ciWPfbKM3jfVj4tJ/PXzPA7kFxTy69fmsGj9Tp65vAddm8fXVTIiIiISkpmRzq/PbM+sNduZuGhTrMMBlMzFnLsz8h8L+eeSzdx/fhfOOPboWIckIiIih3BxdguOaVSTkeMXsmd/fqzDUTIXa89/uZJXpn3HTae148perWIdjoiIiBxGRnoaD1/YhfU78nhi4rJYh6NkLpbempXLIx8uYVC3pow455hYhyMiIiKl1LNVPa44uSVjvlrFgnXRve1iJCVzMfLBvA2MeOsb+nVowB8HH09aWuwHUIqIiEjpjeh/LPVqVOPucfMpiJwbKoqUzMXAZ4s3cdvrc+jZqi7PX9VTNzEXERFJQLWrZ3DfoE7My93B2K9XxywOJXNR9tXy7xn26mw6Na3FmGtOJKuqZocRERFJVAOPb8JpHRvyx4+XsmHHvpjEoGQuinJWb2Poyzm0bVCDv/3qpAq7UbqIiIjEhpnx4AVdKHBn5PiFMYlByVyULFi3g2v/OpMmtTMZe93JuoWSiIhIkmhRL4vbzuzIxwtDt1eMNiVzUTB95VYuHz2NWtUzeGXoyTSsWS3WIYmIiEgFGtqvDcc0qsnv3lvI97v3R7VsJXOV7KMFG7hqzAwa1qzGGzf2ommd6rEOSURERCpYRnoaj13cje17DzD81dn8WFAYtbKVzFWisV+vZtirs+nStBZv3dRH91sVERFJYl2a1ebRwcczY9U2Hnh/UdTK1aWUlcDdeeyTZfz58+WcddzRPH1ZD6pX1fQjIiIiye787s1YuH4no75cSeemtbjkxJaVXqaSuQqWX1DI3ePm82ZOLpee2IIHL+hClXR1gIqIiKSKEeccw+INO7n33YV0aFSTHi3rVmp5yjIq0KadeVzxwnTezMnl1jM78PsLuyqRExERSTFV0tN4+rITaFw7k5vGzmLTzrxKLU+ZRgWZ/O0WBjw5mXm5O3j84m7ccXZHzHSLLhERkVRUJ6sqo67uye79+dw4dhb78wsqrSwlc+VUUOg8PnEZV4+ZQb0aVRl/S18u7NE81mGJiIhIjB3buBaP/bIbc9f+wC1/n8O+A5WT0CmZK4fNu/K46sXpPPXZt1x4QnPeu6UvHRrVjHVYIiIiEifO7dqEkYM68eniTVw6ehpbdlX8HHRK5srA3Xlndi4DnpzM7O+288fBx/PYxd10n1URERE5yDV92/DclT1ZunEnv3j2K5Zv3lWhn69k7ggtWLeDwc99zR1vfkOzulm8d/Mp/DK7RazDEhERkTh2TufGvHFDb/J+LODCZ6cydcX3FfbZSuZKafueA9wzbj6D/jyFNVv38Ojg4xk3rA/HNNZpVRERETm8bi3qMG54XxrVymTImBm8PSu3Qj7X3L1CPigemFl/4EkgHXjB3R851PrZ2dmek5NzyM/cvDOPd+as47kvVrArL58hvVtz21kdqF09o+ICFxERkZSxY9+PDHtlFlNXbOWk1vUY2q8NZx7XiPS0Q8+CYWaz3D37oOXJksyZWTqwDDgbyAVmApe5e4n30ygpmcv7sYBPF2/i7Vm5fLFsC4UOp7RvwL0DO6knTkRERMrtQH4hY6etYcyUVaz7YR+t62dx3SltuKhn8xLH4KdCMtcbGOnu5wSv7wJw99+XtE3Pntn+7sQv2bxrP1t27WfLrjwWb9zF+9+sZ2dePk1qZ3Jhj2Zc2KM57RoeFaWaiIiISKrILyjko4UbGT15Fd+s/YHa1TMY0LUxLevVoFnd6jSrk0mzOlk0rFmNKulpxSZzyXT5ZTNgbdjrXODkQ22wYP0O+j36+U+WZWak0b9zYwb3bEHvdvUP2+UpIiIiUlZV0tMYeHxTft61CbPWbOeFyav4YN4Gdubl/3S9Q+QjyZTMFVfLg7odzewG4Ibg5f41fxi4IHKdpYQG3qWABkDFXU6TmNQGagNQG6R6/UFtAGqDRKh/q+IWJlMylwuEzxHSHFgfuZK7jwJGAZhZTnHdlaki1esPagNQG4DaINXrD2oDUBskcv2TaWqSmUAHM2tjZlWBS4HxMY5JREREpFIlTc+cu+eb2S3Ax4SmJhnj7gtjHJaIiIhIpUqaZA7A3ScAE45gk1GVFUuCSPX6g9oA1AagNkj1+oPaANQGCVv/pJmaRERERCQVJdOYOREREZGUk5TJnJn1N7OlZrbczH5TzPvVzOyN4P3pZtY67L27guVLzeycaMZdkcraBmbW2sz2mdnc4PFctGOvKKVog1PNbLaZ5ZvZ4Ij3hpjZt8FjSPSirjjlrH9B2D6QsBcSlaIN7jCzRWY2z8w+M7NWYe8l/D4A5W6DVNkPbjKz+UE9p5hZp7D3Ev6YUNb6p9LxIGy9wWbmZpYdtiz+9wF3T6oHoYsfVgBtgarAN0CniHWGA88Fzy8F3giedwrWrwa0CT4nPdZ1inIbtAYWxLoOUWqD1sDxwN+AwWHL6wErg3/rBs/rxrpO0ap/8N7uWNchSm1wBpAVPB8W9v8g4feB8rZBiu0HtcKenwd8FDxP+GNCOeufMseDYL2awJfANCA7kfaBZOyZOwlY7u4r3f0A8DpwfsQ65wMvB8/fAs40MwuWv+7u+919FbA8+LxEU542SBaHbQN3X+3u84DCiG3PASa6+zZ33w5MBPpHI+gKVJ76J4vStMHn7r43eDmN0PyUkBz7AJSvDZJFadpgZ9jLGvx7wvlkOCaUp/7JojTHRIAHgEeBvLBlCbEPJGMyV9xtvZqVtI675wM7gPql3DYRlKcNANqY2Rwz+8LM+lV2sJWkPN9lMuwH5a1DppnlmNk0M7ugYkOLmiNtg+uAD8u4bbwqTxtACu0HZnazma0gdDC/9Ui2jXPlqT+kyPHAzE4AWrj7+0e6bTxIqqlJAqW5rVdJ65TqlmAJoDxtsAFo6e5bzawn8K6ZdY745ZYIyvNdJsN+UN46tHT39WbWFvinmc139xUVFFu0lLoNzOxKIBs47Ui3jXPlaQNIof3A3Z8BnjGzy4HfAkNKu22cK0/9U+J4YGZpwBPANUe6bbxIxp650tzW61/rmFkVoDawrZTbJoIyt0HQlbwVwN1nERof0LHSI6545fkuk2E/KFcd3H198O9KYBJwQkUGFyWlagMzOwu4BzjP3fcfybYJoDxtkFL7QZjXgaJeyGTYD8pc/xQ6HtQEugCTzGw10AsYH1wEkRj7QKwH7VX0g1Bv40pCAxWLBjp2jljnZn46+P/N4HlnfjrQcSVxONCxktugYVGdCQ0WXQfUi3WdKqMNwtZ9iYMvgFhFaOB73eB5QrVBOetfF6gWPG8AfEsxg4Xj/VHK/wcnEDpAdYhYnvD7QAW0QSrtBx3Cng8CcoLnCX9MKGf9U+54EKw/iX9fAJEQ+0DMA6ikL24AsCz4A3VPsOx+Qr86ATKB/yM0kHEG0DZs23uC7ZYC58a6LtFuA+AiYGGw884GBsW6LpXYBicS+tW1B9gKLAzb9ldB2ywHro11XaJZf6APMD/YB+YD18W6LpXYBp8Cm4C5wWN8Mu0D5WmDFNsPngz+7s0FPifsQJ8Mx4Sy1j+VjgcR604iSOYSZR/QHSBEREREElgyjpkTERERSRlK5kREREQSmJI5ERERkQSmZE5EREQkgSmZExEREUlgSuZEREREEpiSOREREZEEpmRORMrMzF4ys09jHQeAmU0yMw8eA2MdT6Izsz+FtedvS7H+UWa2zsxOjEJsz5vZnyq7HJFEoWRORJLJ34EmwMTwhWY2wMzmmtl+M1ttZncc7oPMbGRYMhP+aH+kQcW6/GI++1Qze8/M1hwiWft/hNoyt5Qfeyeh20DNLG98pXA/MMzM2kahLJG4p2RORJLJPnff6GE3iw9ulv0e8BHQHRgJPGxmN5Xi81YTSmjCH6uOJKBYl1+Co4BFwAhgY3EruPsud98IFBzuw8wsExgGPF8BsR2Wu68DPgOGR6M8kXinZE4kBZjZz82s0My6hy0bama7zaxXCducHZy63GZmO8zsCzM76TDlZJjZI8HptgNmtsjMLo9YZ5KZvWBm95rZxuDzXzKzGmHrVDezUUG5283sWTP7vZktL0P17wBmuvtv3H2xu78EPE2oJ+lwCoLkMPxx2OQmzso/iLtPcPe73P0NYP9hNzi8/kB14JOiBaX8nieZ2Ytm9qCZbTazH8zsITNLM7PfmdkmM9tiZg8VU+Y44MoKiF0k4SmZE0kB7v4BoZtHPwRgZucTSigGu/u0EjY7CngG6EXopuvfAh+ZWf1DFPUwcD1wO9AFeAV4xczOjFhvMFAPOB24HLiAUC9RkT8A5wNXBeXvoOy9MH0J9YqF+whobWbND7NtczPLDR4fmlmfBCw/Gk4D5rh7fsTyw33PRetkAKcQSnzvBt4ntP/1A/4buNvMzo3YbjrQyMyOq7hqiCQmJXMiqWMEcK6Z3UlobNm17h6ZZPyLu49z9/9z92XuvhC4ATBCvTAHMbMs4Fbg3rDtHiZ0ivGeiNW/c/f/dPclQQyvA/8RfE4N4Ebgbncf7+5L3f0uYHEZ692Eg08lbgx7ryTTgauBAcBlwHZgspmdnWDlR0MbYF0xy0v8nsOscvc7g/1lDKHTvy3cfUSw7GVgHhD5g6BoLJ/GzUnKUzInkiLcPQcYDzwC3Onurx9qfTNrY2ZjzWy5me0EdgK1gVYlbNIeqAp8GbH8C6BzxLK5Ea/XAY0iPieyx/DrQ8VbRl7iG+4fuvub7j7P3Se7++XAFOB/EqV8M+sXnEovetxdEUEXozqQV8zyQ33PRb6JeL2RUPIWuezoiGVF5VUvZYwiSatKrAMQkegIpoz4GZAPbCnFJu8D3wM3A2uBA4SSiaqH2S4yQbFilh0oZpvIH5clJjpHaAPQOGJZUUJR7OD/Q/gauDCBys8hdNFFkW1HWF5pbSF0OjVSab7nH4tZp7hlkdsVlVeafVkkqalnTiQFmNmxwARCvXJ/Bh4ys4xDrF8f6AQ84u4fu/siQj0hkb0j4ZYTGkx/WsTyU4GFRxDuckJJQO+I5cVeqFEKXwHnRCzrD6xx99JOu1HkBEKJbUKU7+773H152KOykrnZHNz7Wtm6ErrSdk6UyxWJO+qZE0lyZtaC0FWGr7n7w2bWEBhKaFzan0vYbDuhHo/rzWwFUB94FNhXUjnuvtfMngIeMLMthE6x/ZLQhQylHufl7nvM7HngQTPbBCwDhgDHUbZemCeAqcEVkWOBk4BfA/9ZtIKZ3QLc4u7Hhi17nFDv5GqgFqELO84O6pNI5R/EzI4idDobQj2tjYMrnXe7e1muGP4QeMzMWrj7kSa7ZXU6MMXdd0apPJG4pZ45kSQW9LB9TKh36DYAd98C/C9wr5nVLG47dy8klIi1IzR+6aVgmw2HKfIeYHSw7kJCU0dc6e6fHWHodwL/IHShxgygbhBDceOyDimYxPYCYCCh8VkPAPe4+3NhqzUAjonYtAnwN0IXXnwSvH+Wu/+jaAUzuyaYdLd1vJZfgmxCPVpzgnJuDp6/cISfA4C7LyZ0tfRVZdn+SJmZEbo6Nirz2onEO3OvqGEpIiKVx8z+CWx394tKeH8SsNzdh0YxpvuBi4BuxUzLkRTlm9lq4AV3f/Aw6/UjdLVqB3ffWxmxhJV1MXAv0L0i5t0TSXTqmRORuGNmXc1siJl1NLMuZvYH4AwO33M0JLhqs9jpUyrBQEKnR6OeyFV2+cHkvbuBlqVZ390nE7oFWJuKjqUY1QhNraNETgT1zIlIHDKzLoQSt+MI/ehcAjzk7u8eYptm/HuaivWV3TuU7IJT9HWDl1vdfXss4xGRkimZExEREUlgOs0qIiIiksCUzImIiIgkMCVzIiIiIglMyZyIiIhIAlMyJyIiIpLAlMyJiIiIJDAlcyIiIiIJTMmciIiISAL7/8vk353YRSRPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standard 1D [11-2](111) fcc path\n",
    "gamma.E_gsf_line_plot(vect=[0.5, 0.5, -1], length_unit=length_unit,\n",
    "                      energyperarea_unit=energyperarea_unit)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
